2DBSCAN with Local Outlier Detection

  • Urja Pandya
  • Vidhi Mistry
  • Anjana Rathwa
  • Himani Kachroo
  • Anjali Jivani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1097)


This research is related to designing a new algorithm which is based on the existing DBSCAN algorithm to improve the quality of clustering. DBSCAN algorithm categorizes each data object as either a core point, a border point or a noise point. These points are classified based on the density determined by the input parameters. However, in DBSCAN algorithm, a border point is designated the same cluster as its core point. This leads to a disadvantage of DBSCAN algorithm which is popularly known as the problem of transitivity. The proposed algorithmーtwo DBSCAN with local outlier detection (2DBSCAN-LOD), tries to address this problem. Average silhouette width score is used here to compare the quality of clusters formed by both algorithms. By testing 2DBSCAN-LOD on different artificial datasets, it is found that the average silhouette width score of clusters formed by DBSCAN-LOD is higher than that of the clusters formed by DBSCAN.


DBSCAN Clustering Border points Local outliers Global outliers 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Urja Pandya
    • 1
  • Vidhi Mistry
    • 1
  • Anjana Rathwa
    • 1
  • Himani Kachroo
    • 1
  • Anjali Jivani
    • 1
  1. 1.Department of Computer Science and EngineeringThe Maharaja Sayajirao University of BarodaVadodaraIndia

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